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Microsoft Power BI Performance Best Practices

You're reading from   Microsoft Power BI Performance Best Practices A comprehensive guide to building consistently fast Power BI solutions

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781801076449
Length 312 pages
Edition 1st Edition
Languages
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Author (1):
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Bhavik Merchant Bhavik Merchant
Author Profile Icon Bhavik Merchant
Bhavik Merchant
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Architecture, Bottlenecks, and Performance Targets
2. Chapter 1: Setting Targets and Identifying Problem Areas FREE CHAPTER 3. Chapter 2: Exploring Power BI Architecture and Configuration 4. Chapter 3: DirectQuery Optimization 5. Part 2: Performance Analysis, Improvement, and Management
6. Chapter 4: Analyzing Logs and Metrics 7. Chapter 5: Desktop Performance Analyzer 8. Chapter 6: Third-Party Utilities 9. Chapter 7: Governing with a Performance Framework 10. Part 3: Fetching, Transforming, and Visualizing Data
11. Chapter 8: Loading, Transforming, and Refreshing Data 12. Chapter 9: Report and Dashboard Design 13. Part 4: Data Models, Calculations, and Large Datasets
14. Chapter 10: Data Modeling and Row-Level Security 15. Chapter 11: Improving DAX 16. Chapter 12: High-Scale Patterns 17. Part 5: Optimizing Premium and Embedded Capacities
18. Chapter 13: Optimizing Premium and Embedded Capacities 19. Chapter 14: Embedding in Applications 20. Other Books You May Enjoy

Establishing a repeatable, pro-active performance improvement process

In Chapter 1, Setting Targets and Identifying Problem Areas, we learned about the potential negative impacts of poor business intelligence system performance. It is great to have knowledge, metrics, and tools to resolve performance issues. However, a behavior that I have seen all too often is that these are usually leveraged reactively after an issue has had enough of an impact on the business that it is formally raised and brought to the attention of developers and administrators. This is not a good situation to be in for reasons described in the following points:

  • Changing production systems is non-trivial, it requires careful change management, and can involve more than just deploying new technical artifacts. One example is that users may need training and documentation may need to be updated if there are significant report or dataset level changes.
  • There may be short deadlines for the business to resolve...
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